Recommendations Based Upon Explicit User Similarity

ABSTRACT

A system and method for providing recommendations to individuals on a social network, in which the recommendations include information indicating the similarity of the individuals to one another, to aid the individuals in judging the degree to which the opinions of the others are applicable to the themselves.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/980,396, filed May 15, 2018, which is a continuation of U.S.patent application Ser. No. 14/206,913, filed Mar. 12, 2014, issued asU.S. Pat. No. 9,970,042 on May 15, 2018, which claims benefit from andpriority to U.S. Provisional Patent Application No. 61/802,338, filedMar. 15, 2013. The above-identified applications are hereby incorporatedherein by reference in their entirety.

FIELD OF THE INVENTION

Certain embodiments of the invention relate to systems and methods thatprovide recommendations. More specifically, certain embodiments of thepresent invention relate to systems and methods for providingrecommendations to individuals on a social network, in which therecommendations include information indicating the similarity of theindividuals to one another, to aid the individuals in judging the degreeto which the opinions of the others are applicable to themselves.

BACKGROUND OF THE INVENTION

Automated recommendations today are frequently based on an analysis ofimplicit patterns of user behavior and are reflected to users in anobscure manner, such as, for example, recommendations in a form such as“people who viewed this also viewed,” that are currently presented toviewers on many e-commerce sites today. This type of information is oflimited use to a viewer, in that it reduces the likelihood that theviewer will truly relate to the recommendation, since they do not knowwho the other “viewers” are, and do not know why what the others viewedis relevant to their own specific case. In another example, the presentviewer may be presented with average ratings that other viewers gave toa certain product. In this situation, the present viewer does not knowif the rating of the other viewers is really relevant for them, as theinterest or needs of the other viewers may be quite different from theinterests or needs of the present viewer.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present invention asset forth in the remainder of the present application with reference tothe drawings.

BRIEF SUMMARY OF THE INVENTION

A system and/or method that provides as part of a recommendation to aconsumer, a calculated measure of similarity between consumers uponwhich the recommendation is based, and the consumer viewing therecommendation, substantially as shown in and/or described in connectionwith at least one of the figures, as set forth more completely in theclaims.

These and other advantages, aspects and novel features of the presentinvention, as well as details of an illustrated embodiment thereof, willbe more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary computer network in which theinventive concepts discussed herein may be practiced, in accordance witha representative embodiment of the present invention.

FIG. 2 is an illustration of an example profile page for a user “TomSmith” as seen by a viewer, in accordance with a representativeembodiment of the present invention.

FIG. 3 is an illustration of an example similarity information windowrepresenting similarity of two users of, for example, an e-commerce website, in accordance with one representative embodiment of the presentinvention.

FIG. 4 is an illustration of an example similarity information screenshowing detailed similarity information for the viewer of the similarityinformation screen and an identified user, with regard to a number oftopics or categories, in accordance with a representative embodiment ofthe present invention.

FIG. 5 is an illustration of an example pop-up window used to displayinformation explaining to a user how similarity of the user and anotherindividual is determined, in accordance with a representative embodimentof the present invention.

FIG. 6 is an illustration of an example “people” discovery menu, inaccordance with a representative embodiment of the present invention.

FIG. 7 is a flowchart for an exemplary method of producing arecommendation for a first user using interaction information for eachof the first user and a second user of a plurality of users of acomputer network, in accordance with a representative embodiment of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

Certain embodiments of the invention relate to systems and methods thatprovide recommendations. More specifically, certain embodiments of thepresent invention relate to systems and methods for providingrecommendations to individuals on a social network, in which therecommendations include information indicating the similarity of theindividuals to one another, to aid the individuals in judging the degreeto which the opinions of the others are applicable to themselves. Thepresent invention improves the degree to which a user can make use ofrecommendation of others by enabling them to rely on a calculation ofsimilarity between users and to use such a calculation to present one ormore indications of similarity between the users on which therecommendation is based, and the user who is viewing the recommendation.

The following description of example systems and methods is not intendedto limit the scope of the description to the precise form or formsdetailed herein. Instead the following description is intended to beillustrative so that others may follow its teachings.

In accordance with a representative embodiment of the present invention,a system provides for the use of commodity hardware, off the shelfsoftware, OS independent applications, form factor independent devices(PC, tablets, smart phones etc), media independent (voice, text, video)and cloud based infrastructure to run all functionalities of the presentsystem. In the context of a service establishment, such as, for example,a retail store this is specifically very useful as a customer canutilize familiar technologies and receive related and personalizedsupport, assistance, product demos, recommendations, suggestions, etc.,which can be handled by a sales associate or customer service agent whohas the most expertise and relevant information and who are locatedproximate to the customer (e.g., on a sales floor, at a businesslocation near the location of the customer, and/or can be handled by theconsumer's social network.

In the following discussion, the term “customer” may be used to refer toa potential or existing purchaser of products and/or services of abusiness. The terms “customer service agent” and “sales associate” maybe used herein interchangeably to refer to an employee that providesproduct and/or sales related assistance to customers of a business. Thesales associate or customer service agent may be, by way of example andnot limitation, an expert, question and answer provider, merchandiseassociate, etc. The term “channel” in the present context may refer tovarious means of communicating such as, for example, onlinecommunication (e.g., Internet-based), mobile communication (e.g.,wireless communication such as cellular or Wi-Fi), and in-store.

The term “personal shopper” may be used herein to refer to an individualthat provides product information, recommendations, and/or purchaseassistance to members of their own social network or others under theguidance and/or with the assistance of the operators of a commercialretail business. In the following discussion, the expression“follow”/“to follow” may, for example, be used to refer to the act of auser requesting to receive updates about the actions of another user, orto be sent messages sent by or information posted by another user, toname just a few examples. The term “tag” may be user herein to refer toa text string or label that may be associated with an item to associateor identify the item with, for example, a particular topic, activity,brand, store, public figure, style, or category of interest.

The terms “cart” or “shopping cart” may be used herein to refer to acollection of items that have been selected for purchase by a user of ane-commerce web site. The term “catalog” may be used herein to refer to,for example, a collection of items selected by a user as favoriteproducts; products being considered for later purchase, and collectionsof user selected products to be shared with friends, family, or membersof a user's social network.

As utilized herein, the terms “exemplary” or “example” mean serving as anon-limiting example, instance, or illustration. As utilized herein, theterm “e.g.” introduces a list of one or more non-limiting examples,instances, or illustrations.

It should be noted that although the following discussion relates to theuse of explicit user similarity in an social e-commerce environment,this does not necessarily represent a specific limitation of the presentinvention, unless explicitly recited in the claims, as the concept ofsimilarity, and recommendations based on explicit user similarity mayapply to any social network or relation between people on a website.

The methods and systems disclosed herein may, for example, be part of anoverall shopping experience system created to enhance the consumershopping event. In one example, the disclosed system may be integratedwith a customer reward system, a customer's social network (e.g., thecustomer can post their shopping activity conducted through the systemto their social network), a customer expert system, digital/mobileapplications, shopping history, wish list, registry, location,merchandise selections, or the like. It will be appreciated, however, byone of ordinary skill in the art that the system disclosed may be fullyand/or partially integrated with any suitable shopping system asdesired, including those not mentioned and/or later designed.

In regard to the following discussion of representative embodiments ofthe present invention, the concept of user similarity may be representedas a value, level, or score that describes how similar one user is toanother, in a quantifiable way. More general background about similarityand how it may be used is disclosed through the following examples,which are intended to be illustrative and not limiting.

In accordance with a representative embodiment of the present invention,similarity may be considered to be a weighted, undirected, and symmetricproperty of a user pair. That is, if a user A is similar to a user B,then user B is also similar to user A, to the same degree, measure, orstrength. In addition, similarity may be calculated either overall, ortopically (e.g., only with regard to a specific aspect or characteristic(e.g., a tag) of any type). For example, a user A may be similar ingeneral to a user B, but when considering a specific aspect or categoryC, user A may be very different (and dissimilar) from user B.

The following discussion describes some examples of information typesupon which a user similarity level may be based, and how a similaritylevel may be calculated, and lays out some example uses for such asimilarity level such as, for example and not limitation, on an onlineprofile page of a user, and in what is referred to herein as a “people”discovery menu. It should be noted that the disclosed examples are forpurposes of illustration and explanation, and are not necessarilyspecific limitations of the present invention, unless explicitly recitedin the claims. It is expected that upon reading and appreciating theteachings of the present disclosure, that future uses of the use ofsimilarity will expand beyond the examples set forth herein.

With reference to the figures, and in particular to the computer networkof FIG. 1 in which a representative embodiment of the present inventionmay be practiced, the following discloses various example systems andmethods for providing recommendations based on explicit user similarity.To this end, a processing device 20″, illustrated in the exemplary formof a mobile communication device, a processing device 20′, illustratedin the exemplary form of a computer system, and a processing device 20illustrated in schematic form, are provided with executable instructionsto, for example, provide a means for a customer, e.g., a user, consumer,etc., to access a host system server 68 and, among other things, beconnected to a hosted social networking site, a user profile, and/or asales associate. Generally, the computer executable instructions residein program modules which may include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Accordingly, those of ordinaryskill in the art will appreciate that the processing devices 20, 20′,20″ illustrated in FIG. 1 may be embodied in any device having theability to execute instructions such as, by way of example, a personalcomputer, mainframe computer, personal-digital assistant (“FDA”),cellular telephone, tablet, e-reader, or the like. Furthermore, whiledescribed and illustrated in the context of a single processing device20, 20′, 20″ those of ordinary skill in the art will also appreciatethat the various tasks described hereinafter may be practiced in adistributed environment having multiple processing devices linked via alocal or wide-area network whereby the executable instructions may beassociated with and/or executed by one or more of multiple processingdevices.

For performing the various tasks in accordance with the executableinstructions, the example processing device 20 includes a processingunit 22 and a system memory 24 which may be linked via a bus 26. Withoutlimitation, the bus 26 may be a memory bus, a peripheral bus, and/or alocal bus using any of a variety of bus architectures. As needed for anyparticular purpose, the system memory 24 may include read only memory(ROM) 28 and/or random access memory (RAM) 30. Additional memory devicesmay also be made accessible to the processing device 20 by means of, forexample, a hard disk drive interface 32, a magnetic disk drive interface34, and/or an optical disk drive interface 36. As will be understood,these devices, which would be linked to the system bus 26, respectivelyallow for reading from and writing to a hard disk 38, reading from orwriting to a removable magnetic disk 40, and for reading from or writingto a removable optical disk 42, such as a CD/DVD ROM or other opticalmedia. The drive interfaces and their associated computer-readable mediaallow for the nonvolatile storage of computer-readable instructions,data structures, program modules and other data for the processingdevice 20. Those of ordinary skill in the art will further appreciatethat other types of non-transitory computer-readable media that canstore data and/or instructions may be used for this same purpose.Examples of such media devices include, but are not limited to, magneticcassettes, flash memory cards, digital videodisks, Bernoulli cartridges,random access memories, nano-drives, memory sticks, and other read/writeand/or read-only memories.

A number of program modules may be stored in one or more of thememory/media devices. For example, a basic input/output system (BIOS)44, containing the basic routines that help to transfer informationbetween elements within the processing device 20, such as duringstart-up, may be stored in ROM 28. Similarly, the RAM 30, hard drive 38,and/or peripheral memory devices may be used to store computerexecutable instructions comprising an operating system 46, one or moreapplications programs 48 (such as a Web browser), other program modules50, and/or program data 52. Still further, computer-executableinstructions may be downloaded to one or more of the computing devicesas needed, for example via a network connection.

To allow a user to enter commands and information into the processingdevice 20, input devices such as a keyboard 54 and/or a pointing device56 are provided. While not illustrated, other input devices may includea microphone, a joystick, a game pad, a scanner, a camera, touchpad,touch screen, etc. These and other input devices would typically beconnected to the processing unit 22 by means of an interface 58 which,in turn, would be coupled to the bus 26. Input devices may be connectedto the processor 22 using interfaces such as, for example, a parallelport, game port, FireWire, or a universal serial bus (USB). To viewinformation from the processing device 20, a monitor 60 or other type ofdisplay device may also be connected to the bus 26 via an interface,such as a video adapter 62. In addition to the monitor 60, theprocessing device 20 may also include other peripheral output devices,not shown, such as, for example, speakers, cameras, printers, or othersuitable device.

As noted, the processing device 20 may also utilize logical connectionsto one or more remote processing devices, such as the host system server68 having associated data repository 68A. In this regard, while the hostsystem server 68 has been illustrated in the exemplary form of acomputer, it will be appreciated that the host system server 68 may,like processing device 20, be any type of device having processingcapabilities. Again, it will be appreciated that the host system server68 need not be implemented as a single device but may be implemented ina manner such that the tasks performed by the host system server 68 aredistributed amongst a plurality of processing devices/databases locatedat different geographical locations and linked through a communicationnetwork. Additionally, the host system server 68 may have logicalconnections to other third party systems via a network 12, such as, forexample, the Internet, LAN, MAN, WAN, cellular network, cloud network,enterprise network, virtual private network, wired and/or wirelessnetwork, or other suitable network, and via such connections, will beassociated with data repositories that are associated with such otherthird party systems. Such third party systems may include, withoutlimitation, systems of banking, credit, or other financial institutions,systems of third party providers of goods and/or services, systems ofshipping/delivery companies, etc.

For performing tasks as needed, the host system server 68 may includemany or all of the elements described above relative to the processingdevice 20. In addition, the host system server 68 would generallyinclude executable instructions for, among other things, performing anyof the calculation or operations described herein, coordinating a socialnetwork, storage of a shopping list, receiving a location of a customervia a mobile device, receiving a request for a service call centerconnection from either a customer or a sales associate, routing therequest via a distributed mobile video call center, providing a servicecall infrastructure for providing the requestor with a distributedcustomer service experience,

Communications between the processing device 20 and the host systemserver 68 may be exchanged via a further processing device, such as anetwork router (not shown), that is responsible for network routing.Communications with the network router may be performed via a networkinterface component 73. Thus, within such a networked environment, e.g.,the Internet, World Wide Web, LAN, cloud, or other like type of wired orwireless network, it will be appreciated that program modules depictedrelative to the processing device 20, or portions thereof, may be storedin the non-transitory memory storage device(s) of the host system server68.

FIG. 2 is an illustration of an example profile page 200 for a user “TomSmith” as seen by a viewer, in accordance with a representativeembodiment of the present invention. A user viewing a web page for aproduct or service on an e-commerce web site may, for example, arrive ata user profile page such as that illustrated in FIG. 2 by, for example,selecting/clicking one of a group of images or identifiers representingindividuals, which may be identified as being similar to the viewer.This enables the viewer to learn more about the selected user, who mayhave been chosen for identification on the product or service web page,because they, for example, commented on, recommended, purchased, orexpressed an interest in a listed product or service.

The example profile page 200 of FIG. 2 comprises a personal informationsection 205 that includes a name field (showing the name of user “TomSmith”), an “Unfollow” link that permits the viewer of the profile page200 to stop “following” user “Tom,” a gender field (showing “Tom” as“male”), and a “Birthday” field (showing the month and day of “Tom's”birth). The profile page 200 also includes a “Mutual Friends” fieldshowing the number of friends shared by “Tom” and the viewer (in thiscase, “Tom” and the viewer have been found to have 41 mutual friends), a“Following Tom” field showing the number of individuals that are“following” user “Tom,” and a “Followed by Tom” field showing the numberof people being followed by user “Tom.” In addition, the profile page200 includes a “Catalogs” field showing the number of catalogs createdby “Tom,” an indication that user “Tom” is “following” the viewer of theprofile page 200, and a “Block Tom” link that permits the viewer toblock user “Tom” from “following” the viewer of the profile page 200.

The example profile page 200 of FIG. 2 also includes a set of tabs(“About Tom”, “Recent Activity”, “Catalogs”, and “More”) that permit theviewer to display additional information about user “Tom.” As shown inthe example of FIG. 2, the “About Tom” tab 220 has been selected and isactive, causing additional personal information about user “Tom” to bedisplayed, including information about “Tom's Activity & Community.”

In addition to the features described above, the information of the“About Tom” tab 220 includes a “You & Tom” region 230, which indicatessimilarities between user “Tom” and the viewer of the profile page 200.For example, the illustration of FIG. 2 includes a “Mutual ShoppingPreferences” section 240 that shows a listing of topics or categories242 indicating that the viewer and “Tom” have very highly similarpreferences in the area of “Diet & Nutrition,” highly similarpreferences in the area of “Baby Gear & Travel,” and medium similarityof preferences in the area of “Men's Clothing.” The viewer of theprofile page 200 may select/click a “How is this calculated?” link 244,to request additional information explaining how the similarity inpreferences of user “Tom” and the viewer of the profile page 200 isactually determined. Selecting/clicking the link 244 may cause displayof a window explaining the determination of similarity, such as theexample shown in FIG. 5, which is discussed in further detail below.Such information may permit the viewer to gain some insight into thefactors and parameters used and the reasoning applied in determining thesimilarity of “Tom” and the viewer for a variety of different topics orcategories. The “Mutual Shopping Preferences” section 240 also includesa link “17 more” 246 that indicates that 17 additional topics,categories, or areas of similarity in preferences between “Tom” and theviewer are available for viewing.

It should be noted that in some representative embodiments of thepresent invention, an indication of similarity of users A and B may notbe presented to user A, if user B is flagged as “private.” It shouldalso be noted that in some representative embodiments of the presentinvention, an indication of similarity of users A and B may not bepresented to user A, if user B is flagged as “friends-only,” and user Adoes not “follow” user B. The “friends-only” flag/status may be adefault setting in some representative embodiments of the presentinvention.

In addition, it should be noted that the features of the example profilepage of FIG. 2 are for illustrative purposes only, and do notnecessarily represent specific limitations of the present invention,unless explicitly recited in the claims, as many different ways ofconveying similarity in user preferences may be employed withoutdeparting from the spirit and scope of the present invention. Forexample, a representative embodiment of the present invention mayprovide an indication of overall similarity as, for example, a numericvalue (e.g., X out of Y), a textual descriptor (e.g., “Highly Similar,”“Very Similar,” . . . “Low Similarity”), a graphic (e.g., a bar whoselength indicates a degree of similarity, a “similarity meter,” a color,a shade of gray, the size of a graphic, etc.) or any other suitableindicator usable as a representation of a degree of similarity of theowner of the profile page 200 and a viewer, as well as individualindications of similarity as a number of topical sub-scores.

FIG. 3 is an illustration of an example similarity information window300 representing similarity of two users of, for example, an e-commerceweb site, in accordance with one representative embodiment of thepresent invention. The example similarity information 300 of FIG. 3includes a first graphic 312, an indication of overall similarity 314,and a second graphic 316. In the example of FIG. 3, one of first graphic312 and the second graphic 316 may, for example, represent a first userof a social e-commerce system. The first user may be viewing thesimilarity information window 300 as part of seeking a recommendationfor a product or service of interest. The other of the first graphic 312and the second graphic 316 may represent a second user that haspreviously commented on or submitted a recommendation on aproduct/service of interest to the first user. The second user may beidentified by a name field 330. Each of the first graphic 312 and thesecond graphic 316 may, for example, be a photograph or a graphicrepresenting the respective user.

In a representative embodiment of the present invention, an indicationof overall similarity may, for example, be represented as a numericvalue (e.g., X out of Y), a textual descriptor (e.g., “Highly Similar,”“Very Similar,” . . . “Low Similarity”), a graphic (e.g., a bar whoselength indicates a degree of similarity, a “similarity meter,” a color,a shade of gray, the size of a graphic, etc.) or any other suitableindication as a representation of a degree of similarity of the viewinguser seeking a relevant recommendation, and the user that previouslycommented on or recommended the product or service of interest to theviewing user.

In addition to the indication of overall similarity 314, arepresentative embodiment of the present invention may include topicalsub-scores 342A, 342B, 342C, which provide indications of how theviewing user and the user that submitted a comment or recommendation aresimilar with respect to one or more topics (e.g., “Craftsman,” “Lawn &Garden,” and “Baby Gear & Travel”) or categories of products. The degreeor strength of similarity in each topic or category may be indicated bya numeric value, as descriptive text (e.g., “Very High,” “High,”“Medium,” etc.), or as any suitable graphical representation.

In addition to the above, the similarity information 300 of FIG. 3 mayalso include a selectable/clickable link 344 to allow the viewer tolearn how the similarity of the user viewing the similarity informationwindow 300 and the user (e.g., “Tom”) is determined. Selecting/clickingthe link 344 may result in the display of a window explaining what isinvolved in determining similarity, such as the example shown in FIG. 5,discussed in greater detail below. The user may also select/click on alink 346 to view similarity information for other users that havecommented on or submitted recommendations for the product or service ofinterest to the viewing user. Detailed information about how similaritymay be calculated is provided below. In a representative embodiment ofthe present invention, if insufficient information is available topresent an indication of similarity, the numeric, textual, or graphicalrepresentation of the strength or degree of similarity described abovemay be replaced by text or graphics indicating, for example,“insufficient data.”

FIG. 4 is an illustration of an example similarity information screen400 showing detailed similarity information for the viewer of thesimilarity information screen 400 and an identified user, with regard toa number of topics or categories, in accordance with a representativeembodiment of the present invention. The screen 400 includes a namefield 430 that identifies the other user with whom similarity to theviewer of the screen 400 has been determined, a listing 410 of topics,categories, or areas of similarity for the viewer and a user identifiedas “Moran,” and images representing product examples from the topics,categories, or areas in which the viewer and user “Moran” exhibitinterest. As shown by the first three lines 442A in the example of FIG.4, the viewer and user “Moran” exhibit “Very high similarity” in theirinterest in “Women's outerwear,” “Baby's needs,” and “Personalelectronics.” The next three lines 442B of FIG. 4 identify “Women'ssports attire,” “Bedroom accessories,” and “Fragrances” as topics inwhich the viewer and user “Moran” exhibit “High similarity” of interest.The next two lines 442C of FIG. 4 show that the viewer of FIG. 4 anduser “Moran” exhibit “Medium similarity” in their interests in “Kitchensmall appliances” and “Books,” while the last two lines 442D of FIG. 4identify their interests in “Men's outerwear” and “Auto accessories” asshowing “Low similarity.”

It should be noted that the number of topics, categories, or areas, andthe number of degrees, levels, or strengths of interest shown in FIG. 4do not necessarily represent specific limitations of the presentinvention, unless explicitly recited by the claims, and may be differentfrom that shown in FIG. 4, without departing from the spirit and scopeof the present invention. For example, in some representativeembodiments of the present invention, the top N topics of mutualinterest of two individuals (where N=an integer greater than or equal toone) that have a determined similarity level above a certain threshold,may be displayed, while others with a lower determined similarity levelmay be hidden from view. Those displayed topics or categories may beaccompanied by a representation of the degree or strength of thesimilarity as described above. In cases where more than N mutualinterests having a similarity level above the threshold exist, a linkmay be displayed that permits the viewing user to access/display acomplete list of interest topics, categories or areas.

FIG. 5 is an illustration of an example pop-up window 550 used todisplay information explaining to a user how similarity of the user andanother individual is determined, in accordance with a representativeembodiment of the present invention. As shown in the illustration ofFIG. 5, the pop-up window 550 may, for example, be accessed via a link544 while viewing a mutual shopping preferences information window 500that identifies those topics, categories, or areas in which the shoppingpreferences of the viewer and another user (in this example, a usernamed “Eui”) are similar, and an indication of the degree or strength ofsimilarity of their shopping preferences. The pop-up window 550 mayprovide a general explanation that similarity in preferences iscalculated by comparing user web site activities such as product pageviews, purchases, responses to online surveys, and other user behaviors.In some representative embodiments, the pop-up window 550 may explain toa user that other users may be able to see whether they have similarpreferences to the user, but may not be permitted to see exactly whatitems others have purchased, or were interested in.

In accordance with a representative embodiment of the present invention,a link such as the link 544 of FIG. 5 may appear, for example, in anyweb page, pop-up window, or other graphical user interface (GUI) elementthrough which the user is being provided information about thesimilarity of their interests and the interests of other users of, forexample, a social e-commerce system such as that supported by theelements of the computer network illustrated in FIG. 1. For example, thelink 544 may correspond to the link 244 on the profile page 200 of FIG.2, or the link 344 on the similarity information window 300 of FIG. 3.It should be noted that FIG. 5 is for illustrative purposed only, anddoes not necessarily represent specific limitations of the presentinvention, unless explicitly recited by the claims.

FIG. 6 is an illustration of an example “people” discovery menu 600, inaccordance with a representative embodiment of the present invention. Inone representative embodiment of the present invention, a “people”discovery menu like that of FIG. 6 may include, for example, a tab(e.g., “People like you” tab 603) or window that may be powered by auser similarity level such as those described herein. The example“people” discovery menu 600 of FIG. 6 includes entries 605A, 605B, 605Cidentifying a number of individuals found to share common interests withthe user viewing the “people” discovery menu 600. As shown in FIG. 6,each of the individual entries 605A, 605B, 605C comprise an image of theindividual (if available), the name of the individual, a brief textpassage about the individual, a “Follow” button to request that theviewer be kept updated on actions, messaging, etc. of the identifiedindividual, and an indication of the numbers of other individualspresently “Following” the identified individual. In a representativeembodiment of the present invention, the personal information displayedin the entries 605A, 605B, 605C may be the same as that displayed on acorresponding profile page for the identified individual such as, forexample, the personal information shown on the profile page 200illustrated in FIG. 2.

In addition to the personal information in the left portion of each ofindividual entries 605A, 605B, 605C, the entries 605A, 605B, 605C alsoinclude at the right end, an example of one approach to displaying asimilarity level 614A representing overall similarity of the individualidentified by the left portion of individual entries 605A, 605B, 605C ofFIG. 6, represented by a first graphic 612A, and the viewer of the“people” discovery menu 600, represented by a second graphic 616A.Although shown as a numeric value in the example of FIG. 6, the degreeor strength of similarity of the individual identified by the entries605A, 605B, 605C to the viewer of the “people” discovery menu 600 shownas similarity level 614A may instead be expressed as a textual phrase, acolor, a shade of gray, a graphic, or any other suitable means. The“people” discovery menu 600 may also include a “See why” or “How is thiscalculated?” link (not shown), similar to those discussed above withrespect to FIG. 2 and FIG. 3, which may, for example, open the same typeof window described above with respect to FIG. 5. In a representativeembodiment of the present invention, the individuals represented on amenu such as, for example, the “people” discovery menu 600 may bedetermined and identified by a first element of the computer network ofFIG. 1, while digital information representing the “people” discoverymenu 600 transmitted to a viewer of FIG. 6 may be generated by adifferent second element of the computer network of FIG. 1, where thefirst element provides an ordered list of user identifiers to the secondelement for use in generating the corresponding information for eachuser identifier on the “people” discovery menu 600.

In some representative embodiments of the present invention, theindividuals represented in a menu such as the “people” discovery menu600 may not be users that the viewer is currently “following,” and maynot be users that have an associated status/flag setting that representa “private” status, or a user having a “friends-only” status that theviewer does not “follow.” In accordance with a representative embodimentof the present invention, those individuals selected for inclusion may,at a minimum, have an overall high similarity level with the viewer.Individuals listed on the “people” discovery menu 600 may be those userswith the highest similarity with the viewer, in order of the degree orstrength of similarity with the viewer. In some representativeembodiments of the present invention, information about topical interestsimilarity may also be available to the viewer of a menu such as the“people” discovery menu 600. Further details about how individuals maybe identified for inclusion in a listing such as the “people” discoverymenu 600 are provided below. It should be noted that, while it ispreferable that the search for individuals similar to the viewer of FIG.6 be exhaustive, an exhaustive search is not required, and satisfactoryresults may be obtained by adhering to the criteria described above.

The following discussion provides further details on how similarity iscalculated in accordance with a representative embodiment of the presentinvention, including the types of data used, how the data may be cleanedand normalized, and how a calculated similarity level or score may betranslated or mapped to a corresponding textual or graphicrepresentation.

A representative embodiment of the present invention may calculate asimilarity level or similarity score in the following manner. Let usassume that we have a set of n users, {U_(i)}_(i=1 . . . n). Each of then users may be characterized by a set of m attributes{A_(j)}_(j=1, . . . m) built with information about each user'sinteraction with an application on a computer network such as, forexample, a social e-commerce application such that may be used on thecomputer network illustrated in FIG. 1 and described above. For example,the computer network of FIG. 1 may be arranged to operate as a sociale-commerce system that enables users to engage in social interactions aswell as, and as a part of, shopping for products and/or services.

In a representative embodiment of the present invention, the strength ofthe relations between users and attributes may be expressed usingweights W_(ij). Thus, each user i may be identified by the set of allattributes weights he/she currently possess. This set of all attributesweights for a user i may be represented by a vector in an m-dimensionalparameter space as: UV_(i)={W_(i,j)}_(j=1 . . . m). In order tocalculate how similar two users are to one another, we may calculate thesimilarity between their respective vectors. While there are severalmethods that may be used, for the purposes of this discussion a methodusing what may be referred to herein as “cosine similarity” will beemployed. It should be noted, however, that the use of this particularmethod of calculating similarity between such vectors does notnecessarily represent a limitation of the present invention, unlessexplicitly recited by the claims, as other suitable methods ofcalculating similarity between such vectors may also be used.

Cosine similarity as used herein represents the angular distance betweentwo vectors. Cosine similarity may be computed by taking the dot-productbetween two vectors divided by their norms, as represented by themathematical expression shown below:

${Sim}_{a,b} = \frac{\sum\limits_{j}{{UV}_{a,j}*{UV}_{b,j}}}{{{UV}_{a}}*{{UV}_{b}}}$

where the norm may be calculated as:

${{UV}_{a}} = \sqrt{\sum\limits_{j = {1{\ldots m}}}( W_{a,j} )^{2}}$

The resulting similarity value, Sim_(a,b), may be expected to fall inthe range [−1,1].

Because the attributes frequency (e.g., user interests) may notnecessarily be evenly distributed, a bias towards more frequentlyoccurring values may be observed. To correct for such bias, arepresentative embodiment of the present invention may use a levelreferred to herein as the Inverse User Frequency (IUF). IUF as usedherein is inverse to the frequency (i.e., the number of distinct usersassociated with an attribute) of the attribute, as shown below:

${IUF}_{j} = {\log \frac{{Number}\mspace{14mu} {of}\mspace{14mu} {users}}{{Number}\mspace{14mu} {of}\mspace{14mu} {users}\mspace{14mu} {with}\mspace{14mu} {attribute}\mspace{14mu} j}}$

The attributes vector for a user i may then be corrected to be:

${UV}_{i} = {{\{ {W_{i,j}{^\circ}\; {IUF}_{j}} \}_{j = {1\ldots \; m}}.{{UV}_{a}}} = \sqrt{\sum\limits_{j = {1\ldots \; m}}( {W_{a,k}{^\circ}\; {IUF}_{j}} )^{2}}}$

In a representative embodiment of the present invention, an overallsimilarity level between users may be calculated by aggregating severalsimilarity levels such as, for example, similarity by user interests,similarity by product preferences, and similarity by using users'answers to online surveys, to name only three possible similarity levelsthat may be used.

A representative embodiment of the present invention may use calculateduser interest to generate attributes vectors for a user. The weight foreach vector element may be the score associated with the user for aspecific interest tag, corrected by the IUF (Inverse User Frequency) forthe tag. To generate a meaningful set of tags, a representativeembodiment of the present invention may perform pre-processing upon taginformation such as, for example, one or more of removing tags whichhave less than a certain number of users associated with the tags (e.g.,at least 2), and removing tags with more than a certain number of userassociated with the tags (e.g., at most ˜10% of the users), and removingattributes that are not relevant in determining user similarity. Arepresentative embodiment of the present invention may also, for eachuser, select the top maxTagsNumber of tags with a ‘weight’ greater thana minInterestWeight as what may be termed “relevant tags,” to limit theamount of data stored, while taking into account the fact that verysmall interests contribute very little to the calculated similarity. Inaddition, an IUF for each tag may be calculated over all tags, accordingto the formula given above. In a representative embodiment of thepresent invention, the collection of tags scores for the relevant tagsmay be used to calculate what may be referred to herein as an “interestsimilarity” value between two users using, for example, the calculationof cosine similarity, discussed above.

For the calculation of user interest similarity to be meaningful, arepresentative embodiment of the present invention may calculate userinterest similarity only for users with at least r relevant interests,where r may be a multiple of 10 tags, as shown below.

Let InterestScore_(j,a) be the weight of interest j for user a:

Sim_(Interest)(a,b)=CosineSimilarity(UV_(a) ^(Interest))

UV_(a) ^(Interest)={Interestscore_(j,a)°IUF_(j)}=_(j=1 . . . m)

A representative embodiment of the present invention may employ aninterest aggregation calculation that may take into account thedifference between explicit and implicit user interactions. For thepurpose of the present discussion, “implicit interactions” may bedefined as those user actions that don't generate User Generated Content(UGC) that a “friend” can see in this user's profile. User actionsconsidered herein to be “implicit interactions” may, for example,include a “page view” or “quick view,” a vote on a online survey, theaddition of an item to a “shopping cart,” a purchase of one or moreitems, and the addition of an item to a “catalog,” to name only a fewexamples. Implicit interactions may be more likely to be consideredsomewhat private by users, even to those considered as friends.

Some representative embodiments of the present invention may givegreater weight to what may be referred to herein as “explicitinteractions” such as, for example, when a user selects an interest andclicks on/selects a button to follow that interest, effectively saying“I am interested in this.” In contrast, the situation where a uservisits a page for a particular interest multiple times but does notexplicitly indicate his/her interest is referred to herein as an“implicit” interest. Some aspects of representative embodiments of thepresent invention such as, for example, those described above withregard to FIG. 5 that provides a general explanation of how a similaritylevel may be calculated, may help to make the relationship of asimilarity level for a pair of users to the activities of those twousers more intuitive to the users.

A representative embodiment of the present invention may use similarityby product preference to generate a more granular view into thesimilarity between two users. Product preference similarity inaccordance with a representative embodiment of the present invention maybe based on mapping the products each user has interacted with, with thefacets relevant to the product. The term “facets” may be used herein torefer to product details that may be product category dependent detailssuch as “brand,” “price,” “color,” “size,” “capacity,” “refresh rate,”“voltage,” and “fabric,” to name only a few. Using such a mapping, aproduct preference vector may be built for each user. Each element ofthe product preference vector may represent a facet relevant to theproduct. The element score may be calculated by summing the userinteraction score with the product facet score for all products attachedto that facet of the product. An attributes vector for productpreference may be defined in the following manner:

Let F_(k) be a specific product facet.Let {P_(l,a)} be the collection of products that share F_(k) and areinterests of user a.We define the product facet score for facet k and user a as:

Product_Facet_Score_(k,a)=Σ_(P) _(l,a) _(∈F) _(k) P _(l,a)

UV_(a) ^(Products)={Product_Facet_Score_(k,a)}_(k=1 . . . m)

Let P_(j,k) be the facet k score of product j. Let W_(i,j) be the user iinteraction score with product j (taking into account the Inverse UsersFrequency for that product). A product facet score may then be computedas:

Product_Facet_Score_(k,a)=Σ_(products) _(j) _(∈facet) _(k) W _(i,j) °P_(j,k)

To generate a meaningful set of facets, a representative embodiment ofthe present invention may perform pre-processing by, for example,calculating similarity only for users with at least minUserNumOfProductnumber of related products. Such pre-processing may use only facets withmore than minFacetNumOfProducts number of related products, and may useonly facets with less than maxFacetNumOfProducts number of relatedproducts. In addition, the pre-processing may, for each user, select thetop maxFacetsNumber with |weight| greater than minFacetWeight.

In addition, an Inverse User Frequency for the product may be calculatedover the products using the formula for IUF, discussed above.

In a representative embodiment of the present invention, a productpreference similarity between two users may be calculated by applyingcosine similarity to the users' facets vectors, as shown below

Sim_(products)(a,b)=CosineSimilarity(UV_(a) ^(Products),UV_(b)^(Products))

As mentioned above, a representative embodiment of the present inventionmay also include a calculation of similarity by online surveys. Forexample, a representative embodiment of the present invention maycalculate similarity between first user, u, and a second user, v, for aspecific tag, t, based on their answers to polls as:

${{Sim}( {u,v,t} )} = {\sum\limits_{p \in {Polls}}\mspace{11mu} {\sum\limits_{a \in {Answers}}{a_{u_{t}}{a_{v_{t}}( {1 - a_{p_{t}}} )}}}}$

where:

a_(u_(t)) = Weight  of  tag  t  in  answer  of  u;a_(v_(t)) = Weight  of  tag  t  in  answer  of  v$a_{p} = \frac{{Number}\mspace{14mu} {of}\mspace{14mu} {users}\mspace{14mu} {voted}\mspace{14mu} a\mspace{14mu} {in}\mspace{14mu} p}{{Number}\mspace{14mu} {of}\mspace{14mu} {users}\mspace{14mu} {voted}\mspace{14mu} {in}\mspace{14mu} p}$$a_{p_{t}} = \frac{{Number}\mspace{14mu} {of}\mspace{14mu} {users}\mspace{14mu} {voted}\mspace{14mu} {an}\mspace{14mu} {answer}\mspace{14mu} {with}\mspace{14mu} {tag}\mspace{14mu} t}{{Number}\mspace{14mu} {of}\mspace{14mu} {users}\mspace{14mu} {voted}\mspace{14mu} {in}\mspace{14mu} p}$

A representative embodiment of the present invention may, for example,calculate similarity between first user, u, and a second user, v, forall tags based on their answers to polls as:

${{Sim}( {u,v} )} = {\frac{2}{{TotalAnswerOfU} + {TotalAnswersOfV}}{\sum\limits_{\underset{{both}\mspace{14mu} u\mspace{14mu} {and}\mspace{14mu} v\mspace{14mu} {voted}\mspace{14mu} a\mspace{14mu} {in}\mspace{14mu} p}{{a \in {Answers}},}}( {1 - a_{p}} )}}$

As previously noted above, in a representative embodiment of the presentinvention, similarity scores or levels may be calculated as numericvalues in the range between 0 (i.e., not similar) and 1 (i.e.,completely similar). The range of similarity score values calculated asshown above, which lie in the interval −1 to +1 may be subdivided,categorized, grouped, or mapped into, for example, a set of textualphrases, having a low-granularity scale, which may be displayed to auser via a user interface such as the listing of topics or categories242 shown in FIG. 2, the similarity information window 300 of FIG. 3, orthe listing of topics, categories, or areas of similarity 410illustrated in FIG. 4. Such textual phrases used to convey the degree orstrength of similarity between users may include, for example, “Veryhigh” to represent user similarity that shows a very high degree ofoverlap (e.g., both users have this specific interest with a highweight), “High,” “Medium,” “Low,” and “Very low” to signify very lowdegree of similarity, but not necessarily dissimilarity. For example,“Very low” may represent that both users have this specific interest,but that one user has it to a very high degree or strength, while theother user has it at a very low degree or strength.

In a representative embodiment of the present invention, the numericsimilarity values may be mapped to the sample scale described above in away that distributes them between these values. Dissimilarity (e.g.,when one user is very interested in X and the other has indicated he/sheis very not interested using the “hide” feature) may be considered ashaving no similarity at all. In accordance with a representativeembodiment of the present invention, the actual divisions or borderssubdividing the range of values of a similarity score or level intocategories or groups may be determined empirically, and may be adjustedaccording to a final distribution of similarity score values.

A representative embodiment of the present invention may calculateoverall similarity using the following formula:

${{sim}( {a,b} )} = {{{Sim}_{interest}( {a,b} )}^{\alpha}*{{Sim}_{products}( {a,b} )}^{\frac{1}{\alpha}}}$

A representative embodiment of the present invention may determine thetop N mutual interests by sorting the values in the mutual interestsscore product and filtering by a minimal threshold:

{InterestScore_(j,a)°InterestScore_(j,b)°IUF_(j)}_(for all pairs j where this value>th)

{InterestScore_(j,a)°InterestScore_(j,b)}_(for all pairs j where this value>th)

In some representative embodiments of the present invention, a list ofsimilar users may be compiled from a predefined list of users that arenot marked or flagged as “private.” The users on the predefined list ofusers may also be those users that are “interesting enough.” By that itis meant that such users present a significant level of interaction withthe system of interest such as, for example, a social e-commerce systemor platform such as may be supported by the computer network of FIG. 1,described above.

In some representative embodiments of the present invention, a metricthat may be used to determine how interesting a user is may be the normof the user interests vector, ∥UV_(a) ^(Interest)∥. A threshold valuemay be defined for this quantity, and only users with norms higher thanthe threshold value may be selected as “interesting enough.” Thisthreshold value may be updated periodically, because the total“interest” in a system such as that described herein may be expected tobe an increasing monotonic function.

A representative embodiment of the present invention may, for a givenuser, calculate a similarity score or level between the given user andeach of the users in an “interesting users list” that may be made up ofusers that have been designated as “interesting enough,” as describedabove. The results of those calculations may be sorted by the similarityscore or level, and the top N users supporting mutual interests may bereturned.

FIG. 7 is a flowchart for an exemplary method of producing arecommendation for a first user using interaction information for eachof the first user and a second user of a plurality of users of acomputer network, in accordance with a representative embodiment of thepresent invention. In a representative embodiment of the presentinvention, the interaction information for the first user and the seconduser may be derived from interactions of the first user and the seconduser with the computer network. The method of FIG. 7 may be performedby, for example, one or more processors in a computer network such asthe computer network illustrated in and described above with respect toFIG. 1.

The method of FIG. 7 begins at block 710, at which the methodcharacterizes the first user and the second user with regard to each ofa plurality of attributes, based upon the interaction information forthe first user and the second user. The method may then, at block 720,express a strength of a relationship between the first user and each ofthe plurality of attributes as a plurality of weights for the firstuser. Next, at block 730, the method may express a strength of arelationship between the second user and each of the plurality ofattributes as a plurality of weights for the second user. The method maythen, at block 740, calculate a level of similarity of the first userand the second user, using the plurality of weights for the first userand the plurality of weights for the second user. Finally, the method ofFIG. 7 may present to the first user an explicit indication representingthe similarity level.

In a representative embodiment of the present invention, recommendationsare based off of similarity of individuals. That is, using a group ofpeople found to be similar at some level to derive other recommendationssuch as, for example, recommending a product to an individual based uponknowledge of products that are popular among people similar to thatindividual. In addition, a representative embodiment of the presentinvention makes similarity of individuals explicit, by showing oneindividual why he/she is similar to another. In this way, arepresentative embodiment of the present invention may explicitly informan individual of the products that have been, for example, viewed bysomeone with tastes similar to the individual.

Aspects of the present invention may be seen in a method of producing arecommendation for a first user using interaction information for eachof the first user and a second user of a plurality of users of acomputer network. In such a method, the interaction information for thefirst user and the second user may be derived from interactions of thefirst user and the second user with the computer network. The methodmay, for example, comprise characterizing the first user and the seconduser with regard to each of a plurality of attributes, based upon theinteraction information for the first user and the second user. Themethod may also comprise expressing a strength of a relationship betweenthe first user and each of the plurality of attributes as a plurality ofweights for the first user, and expressing a strength of a relationshipbetween the second user and each of the plurality of attributes as aplurality of weights for the second user. The method may furthercomprise calculating a level of similarity of the first user and thesecond user, using the plurality of weights for the first user and theplurality of weights for the second user, and presenting to the firstuser an explicit indication representing the similarity level.

In various representative embodiments, the computer network may comprisean e-commerce system, the interaction information may be derived fromsocial network activities of the first user and the second user over thecomputer network, and the interaction information may be derived fromimplicit interactions. In a representative embodiment of the presentinvention, implicit interactions may comprise one or more of: a pageview, a quick view, a vote on a online survey, an addition of an item toan online shopping cart, a purchase of one or more items, and/or anaddition of an item to a private catalog. Calculating the level ofsimilarity of the first user and the second user may comprisecalculating an aggregated similarity level based on user interests andproduct preferences, and calculating the level of similarity of thefirst user and the second user may comprise correcting the plurality ofweights for each of the first user and the second user based upon thenumber of distinct users associated with each attribute.

Additional aspects may be observed in a non-transitory computer-readablemedium having stored thereon a plurality of code sections, each codesection comprising a plurality of instructions executable by a processorto cause the processor to perform a method of producing a recommendationfor a first user using interaction information for each of the firstuser and a second user of a plurality of users of a computer network,the interaction information for the first user and the second userderived from interactions of the first user and the second user with thecomputer network, where the method performed is as described above.

Further aspects of the present invention may be found in a system forproducing a recommendation for a first user using interactioninformation for each of the first user and a second user of a pluralityof users of a computer network, the interaction information for thefirst user and the second user derived from interactions of the firstuser and the second user with the computer network, where the systemperforms the method described above.

Although devices, methods, and systems according to the presentinvention may have been described in connection with a preferredembodiment, it is not intended to be limited to the specific form setforth herein, but on the contrary, it is intended to cover suchalternative, modifications, and equivalents, as can be reasonablyincluded within the scope of the invention as defined by this disclosureand appended diagrams.

Accordingly, the present invention may be realized in hardware,software, or a combination of hardware and software. The presentinvention may be realized in a centralized fashion in at least onecomputer system or in a distributed fashion where different elements arespread across several interconnected computer systems. Any kind ofcomputer system or other apparatus adapted for carrying out the methodsdescribed herein is suited. A typical combination of hardware andsoftware may be a general-purpose computer system with a computerprogram that, when being loaded and executed, controls the computersystem such that it carries out the methods described herein.

The present invention may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the present invention without departing from its scope.Therefore, it is intended that the present invention not be limited tothe particular embodiment disclosed, but that the present invention willinclude all embodiments falling within the scope of the appended claims.

What is claimed is:
 1. (canceled)
 2. A system, wherein the systemcomprises: one or more processors configured to: characterize a firstuser and a second user based on interaction information for the firstuser and the second user via a network; generate a level of similarityof the first user and the second user based on the interactioninformation; provide, to the first user, a recommendation of a productor a service based on the interaction information for the first user andthe second user and the similarity level; provide, to the first user,information relating to the second user so that reasons for thesimilarity can be reviewed by the first user; present, via a selectionof one or more graphical elements of a graphical user interface when theinformation relating to the second user is presented, a list ofcategories and their respective similarity levels; and present, via aselection of one or more graphical elements of the graphical userinterface, how one or more of the similarity levels are calculated. 3.The system according to claim 2, wherein the network comprises ane-commerce system.
 4. The system according to claim 2, wherein theinteraction information is based on online social network activities ofthe first user and the second user via the network.
 5. The systemaccording to claim 2, wherein the interaction information is based onimplicit interactions.
 6. The system according to claim 2, wherein thelevel of similarity of the first user and the second user is generatedaccording to an aggregated similarity level based on user interests andproduct preferences.
 7. The system according to claim 2, wherein therecommendation provided to the first user are according to a level ofsimilarity of a first attribute weight vector and a second attributeweight vector.
 8. The system according to claim 2, wherein theinteraction information is based on one or more of a vote on an onlinesurvey, an addition of an item to an online shopping cart, a purchase ofone or more items, and an addition of an item to a catalog.
 9. Thesystem according to claim 2, wherein the level of similarity of thefirst user and the second user is generated according to an aggregatedsimilarity level based on user interests and product preferences. 10.The system according to claim 2, wherein the level of similarity of thefirst user and the second user is generated according to the aggregatedsimilarity level using answers to online surveys.
 11. The systemaccording to claim 2, wherein the first user and the second user arecharacterized according to attributes.
 12. The system according to claim11, wherein the level of similarity of the first user and the seconduser is generated according to an adjustment of a plurality of weightsfor each of the first user and the second user based upon a number ofdistinct users associated with each attribute.
 13. The system accordingto claim 2, wherein the list of categories and their respectivesimilarity levels are used to determine whether opinions of the seconduser are useful to the first user.
 14. The system according to claim 2,wherein the one or more processors are configured to normalize thesimilarity levels for corresponding categories into different levelcategories.
 15. The system according to claim 14, wherein the normalizedsimilarity levels comprise two or more different level categories. 16.The system according to claim 14, wherein the normalized similaritylevels comprise three or more different level categories.
 17. The systemaccording to claim 2, wherein a privacy aspect of the second user ismaintained.
 18. The system according to claim 17, wherein particularpurchases of the second user are not revealed.
 19. The system accordingto claim 2, wherein the one or more processors are configured to:express a strength of a relationship between the first user and each ofa plurality of attributes as a plurality of weights; and express astrength of a relationship between the second user and each of aplurality of attributes as a plurality of weights.
 20. The systemaccording to claim 19, wherein the one or more processors are configuredto calculate a metric of similarity of the first user and the seconduser according to the weights of the first user and the weights of thesecond user.
 21. The system according to claim 20, wherein the one ormore processors are configured to express a graphical indicationrepresenting the similarity metric.